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Topic: Instrumental variables estimation

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	Instrumental variable - Wikipedia, the free encyclopedia
		An instrument is a variable that does not itself belong in the regression, that is correlated with the suspect explanatory variable, and that is uncorrelated with the error term.
		The instrument cannot be correlated with the error term in the second stage model (that is, the instrument cannot suffer from the same problem as the original predicting variable).
		An instrumental variable is one that is correlated with the independent variable but not with the error term.
	en.wikipedia.org /wiki/Instrumental_variables_estimation (859 words)

	instrumental - HighBeam Encyclopedia
		instrumental in the grammar of certain languages (e.g., Russian), the case referring to means or instrument.
		Instrumental variable estimation in generalized linear measurement error models.
		Instrumental variable estimation in binary regression measurement error models.
	www.encyclopedia.com /doc/1E1-instrmntl.html (289 words)

	Estimation Methods
		The key in all estimation methods is the definition of the counterfactual and measurement of the outcome, that is, what would have been the outcome without the program, at the same point in time.
		Statistical controls or instrumental variables are used in cases when participants and non-participants are compared controlling for other characteristics which may be statistically different between the two groups.
		Another consideration that may affect the choice of the estimation method is the problem of bias, that is, the extent to which various subgroups or target population are likely to participate differently in a program, thus affecting the sample and ultimately the results.
	www1.worldbank.org /prem/poverty/impact/methods/estimation.htm (906 words)

	SEM: Instrumental Variables (David A. Kenny)
		One way of identifying models that cannot be estimated by using multiple regression is through the use of instrumental variables.
		Spuriousness (Third Variable Causation): A variable causes both the endogenous variable and one its causal variables and that variable is not included in the model.
		For this example, variable Q serves as an instrumental variable for Y in the Z equation, and X serves as an instrumental variable for Z in the Y equation.
	davidakenny.net /cm/iv.htm (446 words)

	PcGets Tutorial on Model Formulation and Estimation
		The marked variable that is highest in the database becomes the dependent variable, because it is the first to enter the model.
		Crucially, the instruments must be sufficient in number to identify the equation: i.e., at least as many as right-hand side endogenous variables.
		Moreover, the instruments should be independent of (or at least uncorrelated with) the error on the equation, yet be sufficiently correlated with the endogenous regressors to ensure reasonably-precise estimates.
	www.pcgive.com /pcgets/gtsmfest.html (5385 words)

	SSRN-Instrumental Variables Estimation of Average Treatment Effects in Econometrics and Epidemiology by Joshua Angrist
		Most latent index models commonly applied to qualitative outcomes in econometrics fail to satisfy these conditions, and monte carlo evidence on the bias of instrumental estimates of the average treatment effect in a bivariate probit model is presented.
		The evidence suggests that linear instrumental variables estimators perform nearly as well as the correctly specified maximum likelihood estimator, especially in large samples.
		Linear instrumental variables and the normal maximum likelihood estimator are also remarkably robust to non-normality.
	papers.ssrn.com /sol3/papers.cfm?abstract_id=240090 (487 words)

	Bound: Problems with Instrumental Variables Estimation When the Correlation between the Instruments and the Endogenous ...
		First, the use of instruments that explain little of the variation in the endogenous explanatory variables can lead to large inconsistencies in the IV estimates even if only a weak relationship exists between the instruments and the error in the structural equation.
		The magnitude of the bias of IV estimates approaches that of OLS estimates as the R2 between the instruments and the endogenous explanatory variable approaches 0.
		We suggest that the partial R2 and the F statistic of the identifying instruments in the first-stage estimation are useful indicators of the quality of the IV estimates and should be routinely reported.
	www.psc.isr.umich.edu /pubs/abs.html?ID=623 (402 words)

	Regression Analysis
		In economics, the dependent variable might be a family's consumption expenditure and the independent variables might be the family's income, number of children in the family, and other factors that would affect the family's consumption patterns.
		In political science, the dependent variable might be a state's level of welfare spending and the independent variables measures of public opinion and institutional variables that would cause the state to have higher or lower levels of welfare spending.
		In education, the dependent variable might be a student's score on an achievment test and the independent variables characteristics of the student's family, teachers, or school.
	elsa.berkeley.edu /sst/regression.html (2389 words)

	Variables in a System of Equations (Site not responding. Last check: 2007-10-08)
		Instrumental variables are predetermined variables used in obtaining predicted values for the current period endogenous variables by a first-stage regression.
		The use of instrumental variables characterizes estimation methods such as two-stage least squares and three-stage least squares.
		Instrumental variables estimation methods substitute these first-stage predicted values for endogenous variables when they appear as regressors in model equations.
	www.asu.edu /it/fyi/unix/helpdocs/statistics/sas/sasdoc/sashtml/ets/chap19/sect4.htm (183 words)

	LIMDEP (Site not responding. Last check: 2007-10-08)
		Hausman and Taylor instrumental variable estimator for the linear
		Hausman and Taylor’s estimator for the random effects model overcomes the possible correlation between the independent variables and the random effects.
		You have a choice of covariance structures for ui (uncorrelated, random effect, freely correlated across time) and a choice of different sets of instrumental variables for the GMM estimator (variations in the number of future and lagged values).
	www.limdep.com /new/programfeatures_crosssection_dynamic.shtml (190 words)

	[No title]
		Instrumental Variables Estimation Setting: A class of estimators for regression coefficients.
		Framework: y = X(+ (, K variables in X. There exists a set of K variables, Z such that plim(Z’X/n) (0 but plim(Z’(/n) = 0 The variables in Z are called instrumental variables.
		The least squares estimator might be an IV estimator.
	www.bus.ucf.edu /kim/eco7426/Greene-Notes13.doc (334 words)

	PcGets Help on Model Estimation Statistics
		The estimated variance matrix in (eq:12.45) may exceed, or be less than, that given by the relevant sub-matrix of (eq:12.12), in the sense that the difference could be positive or negative semi-definite.
		For recursive estimation, denote the linear equation by:
		When instrumental variables estimators are used, the recursive formulae are similar to OLS but more cumbersome (see Hendry and Neale, 1987).
	www.pcgive.com /pcgets/gtsest.html (5053 words)

	Recent Papers (Site not responding. Last check: 2007-10-08)
		This paper examines the small-sample distribution of the instrumental variables (IV) estimation procedure employed by Gali and Gertler (1999) to assess the empirical fit of the New Keynesian Phillips Curve (NKPC) and the hybrid Phillips Curve (HPC).
		Their estimation method is now widely used to assess the importance of firms that act in a backward-looking manner.
		Specifically, estimates of the structural parameters of the NKPC model seem reasonable, and the goodness-of-fit test introduced by Gali and Gertler (1999) and Sbordone (2002) suggests that the NKPC does a good job of mirroring inflation dynamics in the traded sector.
	www.georgetown.edu /users/sondergl/papers.htm (1170 words)

	instrumental — Infoplease.com
		wind instrument - wind instrument, in music, any instrument whose tone is produced by a vibrating column of air.
		transposing instrument - transposing instrument, a musical instrument whose part in a score is written at a different pitch...
		On the performance of some robust instrumental variables estimators.
	www.infoplease.com /ce6/society/A0825292.html (231 words)

	Jackknife Instrumental Variables Estimation
		These estimators can be interpreted as instrumental variables procedures using an instrument that is independent of disturbances even in finite samples.
		The new estimators are first-order equivalent to 2SLS but with finite-sample properties superior to those of 2SLS and similar to LIML when there are many instruments.
		Moreover, the jackknife estimators appear to be less sensitive than LIML to deviations from the linear reduced form used in classical simultaneous equations models.
	ideas.repec.org /p/nbr/nberte/0172.html (1487 words)

	[No title]
		Next mark these four variables, and click the Instrument tab on the left-hand side; an I will appear alongside their names to tell the package that it is to treat these four variables as having the status of instruments.
		Ensure that you clear the Instrument status from the four instrumental variables when you formulate the model.
		The OLS parameter estimates of the original model plus the variable RESFIT (ie what you obtain in the regression output when you do the variable addition test) give identical estimates to those obtained by the IV estimation of the original model.
	homepages.strath.ac.uk /~hbs96127/Lab3.doc (877 words)

	Microeconometrics: Methods and Applications by A. Colin Cameron and P.K. Trivedi (Site not responding. Last check: 2007-10-08)
		Chapter 5 presents the most commonly-used estimation methods for nonlinear models, beginning with the quite general topic of m-estimation, before specialization to maximum likelihood and nonlinear least squares regression.
		Chapter 6 provides a comprehensive treatment of generalized method of moments, which is a quite general estimation framework, applicable both in linear and nonlinear, and single- and multi-equation settings.
		Chapter 9 is a stand-alone chapter that presents nonparametric and semiparametric estimation methods that place a flexible structure on the econometric model.
	cameron.econ.ucdavis.edu /mmabook/mmapart2.html (337 words)

	Estimation of Causal Effects Using Instrumental Variables
		A basic idea is that instrumental variables serve as an "experimental handle", turning which may change each individual's treatment status and, through and only through this effect, also change observed outcome.
		For both methods, modelling assumptions are made directly on observed data and separated from the IV assumptions that impose weak restrictions on observed data, while causal effects are inferred by combing observed-data models with the IV assumptions through identification results.
		This approach is flexible enough to host various semiparametric and nonparametric techniques (including model building and checking) that attempt to learn associational relationships from observed data.
	www.bepress.com /jhubiostat/paper76 (324 words)

	Stanislav A. Anatolyev
		Anatolyev's research interests include optimal instruments, bootstrap and empirical likelihood estimation in time series, estimation and inference under asymmetric loss, testing for time series predictability, retrospection and monitoring for structural stability, non-linear time series modeling, and studying dynamics in Russian financial markets.
		His article "The form of the optimal nonlinear instrument for multiperiod conditional moment restrictions" published in Econometric Theory in 2003, derives the process followed by the optimal instrument in the general stationary time series context.
		In the manuscript "Instrumental variables estimation of heteroskedastic linear models using all lags of instruments" (with Kenneth West and Ka-fu Wong) directed to practitioners, the authors develop an algorithm of feasible optimal linear instrumental variables estimator.
	www.nes.ru /english/people/faculty/personal/Anatoliev.htm (521 words)

	Connect: Information Technology at NYU
		It attempts to simplify the model by sequential estimation and reduction of the GUM through eliminating statistically insignificant variables and groups of variables.
		The reliability of the parameters depends on the significance of the variables in one, some, or all of the samples in which the model is tested.
		Often, the variables are tested in different samples before they are conventionally accepted as candidate predictors in the final model.
	www.nyu.edu /its/pubs/connect/spring04/yaffee_pcgets.html (3313 words)

	Is More Better than Less? An Analysis of Children's Mental Health Services Health Services Research - Find Articles (Site not responding. Last check: 2007-10-08)
		The latter provides a means of adjusting comparisons for unobserved or unmeasured differences among individuals receiving differing doses, differences that would otherwise be confounded with the impact of treatment dose.
		Instrumental variables estimation indicates that added outpatient therapy improves functioning among children and adolescents.
		As an alternative means of estimating the impact of dose on outcomes, I propose the use of instrumental variables estimation (IVE).
	findarticles.com /p/articles/mi_m4149/is_5_35/ai_68769338?... (861 words)

	EconPapers: Instrumental Variables Estimation of Quantile Treatment Effects
		Abstract: This paper introduces an instrumental variables estimator for the effect of a binary treatment on the quantiles of potential outcomes.
		The quantile treatment effects (QTE) estimator accommodates exogenous covariates and reduces to quantile regression as a special case when treatment status is exogenous.
		The QTE estimator is illustrated by estimating the effect of childbearing on the distribution of family income.
	econpapers.repec.org /paper/nbrnberte/0229.htm (286 words)

	Institute for Fiscal Studies: Publications
		We consider nonparametric estimation of a regression function that is identified by requiring a specified quantile of the regression "error" conditional on an instrumental variable to be zero.
		The integral operator and distribution of the instrumental variable are unknown and must be estimated nonparametrically.
		We show that the estimator is mean-square consistent, derive its rate of convergence in probability, and give conditions under which this rate is optimal in a minimax sense.
	www.ifs.org.uk /esrc/publications.php?publication_id=3630 (137 words)

	HKUST Institutional Repository: Item 1783.1/1196
		Instrumental variables estimation of a nearly nonstationary error component model with a linear time trend
		Furthermore, autoregressive disturbances are assumed for the error component model, the structure of which may vary with individuals.
		These estimators are shown to have the same normal distribution in the limit.
	hdl.handle.net /1783.1/1196 (142 words)

	INSTRUMENTS Statement
		The INSTRUMENTS statement specifies the instrumental variables to be used in the N2SLS, N3SLS, IT2SLS, IT3SLS, GMM, and ITGMM estimation methods.
		The items specified as instruments for the second form can be variables, names of parameters to be estimated, or the special keyword _EXOG_.
		If you specify the name of a parameter in the instruments list, the partial derivatives of the equations with respect to the parameter (that is, the columns of the Jacobian matrix associated with the parameter) are used as instruments.
	www.asu.edu /it/fyi/unix/helpdocs/statistics/sas/sasdoc/sashtml/ets/chap14/sect20.htm (360 words)

	Abstract for Cowles Foundation Discussion Paper 1547 (Site not responding. Last check: 2007-10-08)
		The present work shows that such "irrelevant" deterministic trend instruments may be systematically used to produce asymptotically efficient estimates of a cointegrated system.
		The approach is convenient in practice, involves only linear instrumental variables estimation, and is a straightforward one step procedure with no loss of degrees of freedom in estimation.
		The procedure is shown to be a form of maximum likelihood estimation where the likelihood is constructed for data projected onto the trending instruments.
	cowles.econ.yale.edu /P/ab/a15/a1547.htm (234 words)

	Judging instrument relevance in instrumental variables estimation
		"Estimating the Euler equation for output," Journal of Monetary Economics, Elsevier, vol.
		"Improved Inference for the Instrumental Variable Estimator," Discussion Papers in Economics at the University of Washington 0039, Department of Economics at the University of Washington.
		"Instrument selection: The case of teenage childbearing and women's educational attainment," Institute for Research on Poverty Discussion Papers 1077-95, University of Wisconsin Institute for Research on Poverty.
	ideas.repec.org /r/fip/fedgfe/94-3.html (1494 words)

	New York University/Econometrics I
		Topics to be studied include specification, estimation, and inference in the context of models that include then extend beyond the standard linear multiple regression framework.
		Develops maximum likelihood estimation of a modification of the linear regression model with a nonnormally distributed disturbance.
		An intriguing study of measurement error and instrumental variables estimation.
	pages.stern.nyu.edu /~wgreene/Econometrics/Outline.htm (2315 words)

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